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[Fix] mmcv.utils -> mmengine.utils () 

* [Fix] mmcv.utils -> mmengine.utils

* mmcv -> mmengine
pull/1303/head
Tong Gao 2022-08-22 14:13:22 +08:00 committed by GitHub
parent 7ac7f66949
commit b0b6dadc00
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GPG Key ID: 4AEE18F83AFDEB23
50 changed files with 196 additions and 162 deletions

4
mmocr/evaluation/metrics/recog_metric.py
View File

@ -3,7 +3,7 @@ import re
from difflib import SequenceMatcher
from typing import Dict, Optional, Sequence, Union
import mmcv
import mmengine
from mmengine.evaluator import BaseMetric
from rapidfuzz.distance import Levenshtein
@ -45,7 +45,7 @@ class WordMetric(BaseMetric):
self.valid_symbol = re.compile(valid_symbol)
if isinstance(mode, str):
mode = [mode]
assert mmcv.is_seq_of(mode, str)
assert mmengine.is_seq_of(mode, str)
assert set(mode).issubset(
{'exact', 'ignore_case', 'ignore_case_symbol'})
self.mode = set(mode)

2
mmocr/models/common/backbones/unet.py
View File

@ -3,8 +3,8 @@ import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, build_norm_layer
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmengine.model import BaseModule
from mmengine.utils.parrots_wrapper import _BatchNorm
from mmocr.registry import MODELS

6
mmocr/models/textdet/postprocessors/base_postprocessor.py
View File

@ -2,7 +2,7 @@
from functools import partial
from typing import Dict, List, Optional, Sequence, Tuple, Union
import mmcv
import mmengine
import numpy as np
from torch import Tensor
@ -138,10 +138,10 @@ class BaseTextDetPostProcessor:
is a tensor, or a list of N lists of tensors if
``pred_results`` is a list of tensors.
"""
assert isinstance(pred_results, Tensor) or mmcv.is_seq_of(
assert isinstance(pred_results, Tensor) or mmengine.is_seq_of(
pred_results, Tensor)
if mmcv.is_seq_of(pred_results, Tensor):
if mmengine.is_seq_of(pred_results, Tensor):
for i in range(1, len(pred_results)):
assert pred_results[0].shape[0] == pred_results[i].shape[0], \
'The first dimension of all tensors should be the same'

4
mmocr/models/textrecog/postprocessors/base_textrecog_postprocessor.py
View File

@ -2,7 +2,7 @@
import warnings
from typing import Dict, Optional, Sequence, Tuple, Union
import mmcv
import mmengine
import torch
from mmengine.data import LabelData
@ -48,7 +48,7 @@ class BaseTextRecogPostprocessor:
'end': self.dictionary.end_idx,
'unknown': self.dictionary.unknown_idx,
}
if not mmcv.is_list_of(ignore_chars, str):
if not mmengine.is_list_of(ignore_chars, str):
raise TypeError('ignore_chars must be list of str')
ignore_indexes = list()
for ignore_char in ignore_chars:

11
mmocr/utils/__init__.py
View File

@ -1,6 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
from .bbox_utils import (bbox2poly, bbox_center_distance, bbox_diag_distance,
bezier2polygon, is_on_same_line, rescale_bboxes,
stitch_boxes_into_lines)
@ -28,11 +26,10 @@ from .typing import (ColorType, ConfigType, DetSampleList, InitConfigType,
RecForwardResults, RecSampleList)
__all__ = [
'Registry', 'build_from_cfg', 'collect_env', 'is_3dlist', 'is_type_list',
'is_none_or_type', 'equal_len', 'is_2dlist', 'valid_boundary',
'list_to_file', 'list_from_file', 'is_on_same_line',
'stitch_boxes_into_lines', 'StringStripper', 'bezier2polygon',
'sort_points', 'dump_ocr_data', 'recog_anno_to_imginfo',
'collect_env', 'is_3dlist', 'is_type_list', 'is_none_or_type', 'equal_len',
'is_2dlist', 'valid_boundary', 'list_to_file', 'list_from_file',
'is_on_same_line', 'stitch_boxes_into_lines', 'StringStripper',
'bezier2polygon', 'sort_points', 'dump_ocr_data', 'recog_anno_to_imginfo',
'rescale_polygons', 'rescale_polygon', 'rescale_bboxes', 'bbox2poly',
'crop_polygon', 'is_poly_inside_rect', 'poly2bbox', 'poly_intersection',
'poly_iou', 'poly_make_valid', 'poly_union', 'poly2shapely',

4
mmocr/utils/collect_env.py
View File

@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
from mmengine.utils import collect_env as collect_base_env
from mmengine.utils import get_git_hash
import mmocr

4
mmocr/utils/fileio.py
View File

@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
import os
import mmcv
import mmengine
def list_to_file(filename, lines):
@ -11,7 +11,7 @@ def list_to_file(filename, lines):
filename (str): The output filename. It will be created/overwritten.
lines (list(str)): Data to be written.
"""
mmcv.mkdir_or_exist(os.path.dirname(filename))
mmengine.mkdir_or_exist(os.path.dirname(filename))
with open(filename, 'w', encoding='utf-8') as fw:
for line in lines:
fw.write(f'{line}\n')

2
mmocr/utils/img_utils.py
View File

@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from mmcv.utils import is_seq_of
from mmengine.utils import is_seq_of
from shapely.geometry import LineString, Point
from .bbox_utils import bbox_jitter

4
tools/analysis_tools/browse_dataset.py
View File

@ -2,7 +2,7 @@
import argparse
import os.path as osp
import mmcv
import mmengine
from mmengine import Config, DictAction
from mmocr.registry import DATASETS, VISUALIZERS
@ -50,7 +50,7 @@ def main():
dataset = DATASETS.build(cfg.train_dataloader.dataset)
visualizer = VISUALIZERS.build(cfg.visualizer)
progress_bar = mmcv.ProgressBar(len(dataset))
progress_bar = mmengine.ProgressBar(len(dataset))
for item in dataset:
img = item['inputs'].permute(1, 2, 0).numpy()
data_sample = item['data_sample'].numpy()

11
tools/dataset_converters/common/curvedsyntext_converter.py
View File

@ -3,7 +3,6 @@ import argparse
import os.path as osp
from functools import partial
import mmcv
import mmengine
import numpy as np
@ -84,14 +83,14 @@ def convert_annotations(data,
start_img_id=start_img_id,
start_ann_id=start_ann_id)
if nproc > 1:
data['annotations'] = mmcv.track_parallel_progress(
data['annotations'] = mmengine.track_parallel_progress(
modify_annotation_with_params, data['annotations'], nproc=nproc)
data['images'] = mmcv.track_parallel_progress(
data['images'] = mmengine.track_parallel_progress(
modify_image_info_with_params, data['images'], nproc=nproc)
else:
data['annotations'] = mmcv.track_progress(
data['annotations'] = mmengine.track_progress(
modify_annotation_with_params, data['annotations'])
data['images'] = mmcv.track_progress(
data['images'] = mmengine.track_progress(
modify_image_info_with_params,
data['images'],
)
@ -103,7 +102,7 @@ def main():
args = parse_args()
root_path = args.root_path
out_dir = args.out_dir if args.out_dir else root_path
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
anns = mmengine.load(osp.join(root_path, 'train1.json'))
data1 = convert_annotations(anns, 'syntext_word_eng', args.num_sample,

12
tools/dataset_converters/common/labelme_converter.py
View File

@ -33,7 +33,7 @@ def parse_labelme_json(json_file,
src_img = mmcv.imread(img_full_path)
img_basename = osp.splitext(img_file)[0]
sub_dir = osp.join(out_dir, 'crops', img_basename)
mmcv.mkdir_or_exist(sub_dir)
mmengine.mkdir_or_exist(sub_dir)
det_line_json_list = []
recog_crop_line_str_list = []
@ -143,7 +143,7 @@ def process(json_dir,
nproc=1,
recog_format='jsonl',
warp=False):
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
json_file_list = glob.glob(osp.join(json_dir, '*.json'))
@ -156,10 +156,10 @@ def process(json_dir,
warp_flag=warp)
if nproc <= 1:
total_results = mmcv.track_progress(parse_labelme_json_func,
json_file_list)
total_results = mmengine.track_progress(parse_labelme_json_func,
json_file_list)
else:
total_results = mmcv.track_parallel_progress(
total_results = mmengine.track_parallel_progress(
parse_labelme_json_func,
json_file_list,
keep_order=True,
@ -174,7 +174,7 @@ def process(json_dir,
total_recog_crop_line_str.extend(res[1])
total_recog_warp_line_str.extend(res[2])
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
det_out_file = osp.join(out_dir, 'instances_training.txt')
list_to_file(det_out_file, total_det_line_json_list)

4
tools/dataset_converters/kie/closeset_to_openset.py
View File

@ -3,7 +3,7 @@ import argparse
import json
from functools import partial
import mmcv
import mmengine
from mmocr.utils import list_from_file, list_to_file
@ -89,7 +89,7 @@ def process(closeset_file, openset_file, merge_bg_others=False, n_proc=10):
convert_func = partial(convert, merge_bg_others=merge_bg_others)
openset_lines = mmcv.track_parallel_progress(
openset_lines = mmengine.track_parallel_progress(
convert_func, closeset_lines, nproc=n_proc)
list_to_file(openset_file, openset_lines)

7
tools/dataset_converters/textdet/bid_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -50,10 +51,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -164,7 +165,7 @@ def parse_args():
def main():
args = parse_args()
root_path = args.root_path
with mmcv.Timer(print_tmpl='It takes {}s to convert BID annotation'):
with mmengine.Timer(print_tmpl='It takes {}s to convert BID annotation'):
files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
image_infos = collect_annotations(files, nproc=args.nproc)

10
tools/dataset_converters/textdet/ctw1500_converter.py
View File

@ -6,6 +6,7 @@ import xml.etree.ElementTree as ET
from functools import partial
import mmcv
import mmengine
import numpy as np
from shapely.geometry import Polygon
@ -72,10 +73,10 @@ def collect_annotations(files, split, nproc=1):
load_img_info_with_split = partial(load_img_info, split=split)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info_with_split, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info_with_split, files)
images = mmengine.track_progress(load_img_info_with_split, files)
return images
@ -208,7 +209,7 @@ def main():
args = parse_args()
root_path = args.root_path
out_dir = args.out_dir if args.out_dir else root_path
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
img_dir = osp.join(root_path, 'imgs')
gt_dir = osp.join(root_path, 'annotations')
@ -220,7 +221,8 @@ def main():
for split, json_name in set_name.items():
print(f'Converting {split} into {json_name}')
with mmcv.Timer(print_tmpl='It takes {}s to convert icdar annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert icdar annotation'):
files = collect_files(
osp.join(img_dir, split), osp.join(gt_dir, split), split)
image_infos = collect_annotations(files, split, nproc=args.nproc)

7
tools/dataset_converters/textdet/detext_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
import numpy as np
from mmocr.utils import dump_ocr_data
@ -50,10 +51,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -146,7 +147,7 @@ def main():
for split in ['training', 'val']:
print(f'Processing {split} set...')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert DeText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),

7
tools/dataset_converters/textdet/funsd_converter.py
View File

@ -51,10 +51,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -144,7 +144,8 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert FUNSD annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
osp.join(root_path, 'annotations', split))

7
tools/dataset_converters/textdet/ic11_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from PIL import Image
from mmocr.utils import dump_ocr_data
@ -68,10 +69,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -158,7 +159,7 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert annotation'):
with mmengine.Timer(print_tmpl='It takes {}s to convert annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))

8
tools/dataset_converters/textdet/ic13_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -51,10 +52,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -151,7 +152,8 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert IC13 annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert IC13 annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split), split)

10
tools/dataset_converters/textdet/icdar_converter.py
View File

@ -5,6 +5,7 @@ import os.path as osp
from functools import partial
import mmcv
import mmengine
import numpy as np
from shapely.geometry import Polygon
@ -62,10 +63,10 @@ def collect_annotations(files, dataset, nproc=1):
load_img_info_with_dataset = partial(load_img_info, dataset=dataset)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info_with_dataset, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info_with_dataset, files)
images = mmengine.track_progress(load_img_info_with_dataset, files)
return images
@ -159,7 +160,7 @@ def main():
args = parse_args()
icdar_path = args.icdar_path
out_dir = args.out_dir if args.out_dir else icdar_path
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
img_dir = osp.join(icdar_path, 'imgs')
gt_dir = osp.join(icdar_path, 'annotations')
@ -171,7 +172,8 @@ def main():
for split, json_name in set_name.items():
print(f'Converting {split} into {json_name}')
with mmcv.Timer(print_tmpl='It takes {}s to convert icdar annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert icdar annotation'):
files = collect_files(
osp.join(img_dir, split), osp.join(gt_dir, split))
image_infos = collect_annotations(

7
tools/dataset_converters/textdet/ilst_converter.py
View File

@ -5,6 +5,7 @@ import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -52,10 +53,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -185,7 +186,7 @@ def parse_args():
def main():
args = parse_args()
root_path = args.root_path
with mmcv.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
with mmengine.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
image_infos = collect_annotations(files, nproc=args.nproc)

3
tools/dataset_converters/textdet/imgur_converter.py
View File

@ -139,7 +139,8 @@ def main():
for split in ['train', 'val', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert IMGUR annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert IMGUR annotation'):
anno_infos = collect_imgur_info(
root_path, f'imgur5k_annotations_{split}.json')
dump_ocr_data(anno_infos,

9
tools/dataset_converters/textdet/kaist_converter.py
View File

@ -6,6 +6,7 @@ import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -66,10 +67,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -181,7 +182,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Training annotation'):
dump_ocr_data(trn_infos, osp.join(root_path,
'instances_training.json'),
@ -190,7 +191,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Val annotation'):
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')

8
tools/dataset_converters/textdet/lv_converter.py
View File

@ -5,6 +5,7 @@ import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -59,10 +60,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -168,7 +169,8 @@ def main():
for split in ['train', 'val', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert LV annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert LV annotation'):
files = collect_files(osp.join(root_path, 'imgs', split))
image_infos = collect_annotations(files, nproc=args.nproc)
dump_ocr_data(image_infos,

9
tools/dataset_converters/textdet/mtwi_converter.py
View File

@ -6,6 +6,7 @@ import os.path as osp
import cv2
import mmcv
import mmengine
from PIL import Image
from mmocr.utils import dump_ocr_data
@ -79,10 +80,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -183,7 +184,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Training annotation'):
dump_ocr_data(trn_infos, osp.join(root_path,
'instances_training.json'),
@ -192,7 +193,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Val annotation'):
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')

7
tools/dataset_converters/textdet/naf_converter.py
View File

@ -61,10 +61,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -182,7 +182,8 @@ def main():
split_info['val'] = split_info.pop('valid')
for split in ['training', 'val', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert NAF annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert NAF annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
osp.join(root_path, 'annotations'), split_info[split])

9
tools/dataset_converters/textdet/rctw_converter.py
View File

@ -5,6 +5,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -63,10 +64,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -172,7 +173,7 @@ def main():
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
# Train set
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Training annotation'):
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
dump_ocr_data(trn_infos, osp.join(root_path,
@ -181,7 +182,7 @@ def main():
# Val set
if len(val_files) > 0:
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Val annotation'):
val_infos = collect_annotations(val_files, nproc=args.nproc)
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),

8
tools/dataset_converters/textdet/rects_converter.py
View File

@ -64,10 +64,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -188,7 +188,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert ReCTS Training annotation'):
dump_ocr_data(trn_infos, osp.join(root_path,
'instances_training.json'),
@ -197,7 +197,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert ReCTS Val annotation'):
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')

8
tools/dataset_converters/textdet/sroie_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
import numpy as np
from mmocr.utils import dump_ocr_data
@ -56,10 +57,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -152,7 +153,8 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert SROIE annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert SROIE annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))

3
tools/dataset_converters/textdet/synthtext_converter.py
View File

@ -6,6 +6,7 @@ import time
import lmdb
import mmcv
import mmengine
import numpy as np
from scipy.io import loadmat
from shapely.geometry import Polygon
@ -169,7 +170,7 @@ def main():
args = parse_args()
synthtext_path = args.synthtext_path
out_dir = args.out_dir if args.out_dir else synthtext_path
mmcv.mkdir_or_exist(out_dir)
mmengine.mkdir_or_exist(out_dir)
gt_name = osp.join(synthtext_path, 'gt.mat')
lmdb_name = 'synthtext.lmdb'

7
tools/dataset_converters/textdet/totaltext_converter.py
View File

@ -7,6 +7,7 @@ import re
import cv2
import mmcv
import mmengine
import numpy as np
import scipy.io as scio
import yaml
@ -64,10 +65,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -396,7 +397,7 @@ def main():
for split, json_name in set_name.items():
print(f'Converting {split} into {json_name}')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert totaltext annotation'):
files = collect_files(
osp.join(img_dir, split), osp.join(gt_dir, split))

7
tools/dataset_converters/textdet/vintext_converter.py
View File

@ -4,6 +4,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
@ -50,10 +51,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -155,7 +156,7 @@ def main():
root_path = args.root_path
for split in ['training', 'test', 'unseen_test']:
print(f'Processing {split} set...')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert VinText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),

7
tools/dataset_converters/textrecog/bid_converter.py
View File

@ -5,6 +5,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils.fileio import list_to_file
from mmocr.utils.img_utils import crop_img
@ -52,10 +53,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -244,7 +245,7 @@ def parse_args():
def main():
args = parse_args()
root_path = args.root_path
with mmcv.Timer(print_tmpl='It takes {}s to convert BID annotation'):
with mmengine.Timer(print_tmpl='It takes {}s to convert BID annotation'):
files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
image_infos = collect_annotations(files, nproc=args.nproc)

6
tools/dataset_converters/textrecog/cocotext_converter.py
View File

@ -138,8 +138,8 @@ def convert_cocotext(root_path,
dst_image_root = osp.join(root_path, 'crops', split)
ignore_image_root = osp.join(root_path, 'ignores', split)
src_image_root = osp.join(root_path, 'imgs')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
process_img_with_path = partial(
process_img,
@ -155,7 +155,7 @@ def convert_cocotext(root_path,
ann_ids = annotation['imgToAnns'][str(img_info['id'])]
anns = [annotation['anns'][str(ann_id)] for ann_id in ann_ids]
tasks.append((img_idx + img_start_idx, img_info, anns))
labels_list = mmcv.track_parallel_progress(
labels_list = mmengine.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []
for label_list in labels_list:

11
tools/dataset_converters/textrecog/detext_converter.py
View File

@ -5,6 +5,7 @@ import os
import os.path as osp
import mmcv
import mmengine
import numpy as np
from mmocr.utils.fileio import list_to_file
@ -52,10 +53,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -141,8 +142,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -211,7 +212,7 @@ def main():
for split in ['training', 'val']:
print(f'Processing {split} set...')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert DeText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),

7
tools/dataset_converters/textrecog/funsd_converter.py
View File

@ -53,10 +53,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -212,7 +212,8 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert FUNSD annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
osp.join(root_path, 'annotations', split))

7
tools/dataset_converters/textrecog/hiertext_converter.py
View File

@ -6,6 +6,7 @@ import os.path as osp
from functools import partial
import mmcv
import mmengine
import numpy as np
from shapely.geometry import Polygon
@ -180,8 +181,8 @@ def convert_hiertext(
dst_image_root = osp.join(root_path, 'crops', split)
ignore_image_root = osp.join(root_path, 'ignores', split)
src_image_root = osp.join(root_path, 'imgs', split)
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
process_img_with_path = partial(
process_img,
@ -195,7 +196,7 @@ def convert_hiertext(
tasks = []
for img_idx, img_info in enumerate(annotation):
tasks.append((img_idx, img_info))
labels_list = mmcv.track_parallel_progress(
labels_list = mmengine.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []

7
tools/dataset_converters/textrecog/ilst_converter.py
View File

@ -6,6 +6,7 @@ import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils.fileio import list_to_file
from mmocr.utils.img_utils import crop_img
@ -54,10 +55,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -258,7 +259,7 @@ def parse_args():
def main():
args = parse_args()
root_path = args.root_path
with mmcv.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
with mmengine.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
image_infos = collect_annotations(files, nproc=args.nproc)

3
tools/dataset_converters/textrecog/imgur_converter.py
View File

@ -177,7 +177,8 @@ def main():
for split in ['train', 'val', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert IMGUR annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert IMGUR annotation'):
anno_infos = collect_imgur_info(
root_path, f'imgur5k_annotations_{split}.json')
generate_ann(root_path, split, anno_infos, args.format)

13
tools/dataset_converters/textrecog/kaist_converter.py
View File

@ -7,6 +7,7 @@ import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils.fileio import list_to_file
from mmocr.utils.img_utils import crop_img
@ -68,10 +69,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -178,8 +179,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -255,7 +256,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Training annotation'):
generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
args.format)
@ -263,7 +264,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Val annotation'):
generate_ann(root_path, 'val', val_infos, args.preserve_vertical,
args.format)

6
tools/dataset_converters/textrecog/lsvt_converter.py
View File

@ -121,8 +121,8 @@ def convert_lsvt(root_path,
dst_image_root = osp.join(root_path, 'crops', split)
ignore_image_root = osp.join(root_path, 'ignores', split)
src_image_root = osp.join(root_path, 'imgs')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
process_img_with_path = partial(
process_img,
@ -163,7 +163,7 @@ def convert_lsvt(root_path,
tasks.append((img_idx + img_start_idx, img_info, annotation[prefix]))
idx = idx + 1
labels_list = mmcv.track_parallel_progress(
labels_list = mmengine.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []
for label_list in labels_list:

13
tools/dataset_converters/textrecog/mtwi_converter.py
View File

@ -7,6 +7,7 @@ import os.path as osp
import cv2
import mmcv
import mmengine
from PIL import Image
from mmocr.utils.fileio import list_to_file
@ -81,10 +82,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -172,8 +173,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -249,7 +250,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Training annotation'):
generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
args.format)
@ -257,7 +258,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Val annotation'):
generate_ann(root_path, 'val', val_infos, args.preserve_vertical,
args.format)

11
tools/dataset_converters/textrecog/naf_converter.py
View File

@ -64,10 +64,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -194,8 +194,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'test_label.{format}')
else:
raise NotImplementedError
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -274,7 +274,8 @@ def main():
split_info['val'] = split_info.pop('valid')
for split in ['training', 'val', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert NAF annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert NAF annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
osp.join(root_path, 'annotations'), split_info[split])

3
tools/dataset_converters/textrecog/openvino_converter.py
View File

@ -5,7 +5,6 @@ import os.path as osp
from argparse import ArgumentParser
from functools import partial
import mmcv
import mmengine
from PIL import Image
@ -80,7 +79,7 @@ def convert_openimages(root_path,
anns.setdefault(ann['image_id'], []).append(ann)
for img_idx, img_info in enumerate(annotation['images']):
tasks.append((img_idx + img_start_idx, img_info, anns[img_info['id']]))
labels_list = mmcv.track_parallel_progress(
labels_list = mmengine.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []
for label_list in labels_list:

13
tools/dataset_converters/textrecog/rctw_converter.py
View File

@ -6,6 +6,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils.fileio import list_to_file
from mmocr.utils.img_utils import crop_img
@ -65,10 +66,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -159,8 +160,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -233,7 +234,7 @@ def main():
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
# Train set
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Training annotation'):
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
@ -241,7 +242,7 @@ def main():
# Val set
if len(val_files) > 0:
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Val annotation'):
val_infos = collect_annotations(val_files, nproc=args.nproc)
generate_ann(root_path, 'val', val_infos, args.preserve_vertical,

12
tools/dataset_converters/textrecog/rects_converter.py
View File

@ -66,10 +66,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -178,8 +178,8 @@ def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
mmengine.mkdir_or_exist(dst_image_root)
mmengine.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
@ -255,7 +255,7 @@ def main():
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert ReCTS Training annotation'):
generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
args.format)
@ -263,7 +263,7 @@ def main():
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert ReCTS Val annotation'):
generate_ann(root_path, 'val', val_infos, args.preserve_vertical,
args.format)

8
tools/dataset_converters/textrecog/sroie_converter.py
View File

@ -5,6 +5,7 @@ import os
import os.path as osp
import mmcv
import mmengine
import numpy as np
from mmocr.utils.fileio import list_to_file
@ -58,10 +59,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -207,7 +208,8 @@ def main():
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert SROIE annotation'):
with mmengine.Timer(
print_tmpl='It takes {}s to convert SROIE annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))

6
tools/dataset_converters/textrecog/synthtext_converter.py
View File

@ -4,6 +4,7 @@ import os
from functools import partial
import mmcv
import mmengine
import numpy as np
from scipy.io import loadmat
@ -80,7 +81,7 @@ def load_gt_data(filename, n_proc):
txt = mat_data['txt']
wordBB = mat_data['wordBB']
charBB = mat_data['charBB']
return mmcv.track_parallel_progress(
return mmengine.track_parallel_progress(
load_gt_datum, list(zip(imnames, txt, wordBB, charBB)), nproc=n_proc)
@ -136,7 +137,8 @@ def main():
process_with_outdir = partial(
process, img_path_prefix=args.img_path, out_dir=args.out_dir)
print('Creating cropped images and gold labels...')
mmcv.track_parallel_progress(process_with_outdir, data, nproc=args.n_proc)
mmengine.track_parallel_progress(
process_with_outdir, data, nproc=args.n_proc)
print('Done')

2
tools/dataset_converters/textrecog/textocr_converter.py
View File

@ -75,7 +75,7 @@ def convert_textocr(root_path,
ann_ids = annotation['imgToAnns'][img_info['id']]
anns = [annotation['anns'][ann_id] for ann_id in ann_ids]
tasks.append((img_idx + img_start_idx, img_info, anns))
labels_list = mmcv.track_parallel_progress(
labels_list = mmengine.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []
for label_list in labels_list:

7
tools/dataset_converters/textrecog/totaltext_converter.py
View File

@ -6,6 +6,7 @@ import os.path as osp
import re
import mmcv
import mmengine
import numpy as np
import scipy.io as scio
import yaml
@ -65,10 +66,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -371,7 +372,7 @@ def main():
for split, ann_name in set_name.items():
print(f'Converting {split} into {ann_name}')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert totaltext annotation'):
files = collect_files(
osp.join(img_dir, split), osp.join(gt_dir, split))

7
tools/dataset_converters/textrecog/vintext_converter.py
View File

@ -5,6 +5,7 @@ import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils.fileio import list_to_file
from mmocr.utils.img_utils import crop_img
@ -52,10 +53,10 @@ def collect_annotations(files, nproc=1):
assert isinstance(nproc, int)
if nproc > 1:
images = mmcv.track_parallel_progress(
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmcv.track_progress(load_img_info, files)
images = mmengine.track_progress(load_img_info, files)
return images
@ -217,7 +218,7 @@ def main():
root_path = args.root_path
for split in ['training', 'test', 'unseen_test']:
print(f'Processing {split} set...')
with mmcv.Timer(
with mmengine.Timer(
print_tmpl='It takes {}s to convert VinText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),